Safety Check — Risk Awareness with Variable Weight
The Problem You Solve
Agents optimize for "it works" — but never ask "what breaks?". A rename is harmless. A schema change on a public API can break 50 consumers. A migration that drops a column destroys data that no git revert can recover. The agent doesn't distinguish between these scenarios because it has no risk radar.
The typical mitigation is "be careful" — a vague instruction that produces vague caution. The agent either ignores it (nothing changes) or applies it uniformly (everything gets a warning, nothing gets real analysis). Both outcomes are useless.
This skill replaces vague caution with dimensional investigation at variable depth. Instead of "be careful", the agent learns to ask "what can go wrong in THIS specific dimension, and can I verify it RIGHT NOW?". It transforms reflection into active research with evidence, scaling effort to match real risk — not perceived risk.
Weight Model
The skill operates at three weights. The weight determines how deep the analysis goes. Start at Light by default; escalate when signals warrant it.
| Weight |
Trigger |
What it does |
Output |
| Light (1) |
Any non-trivial task. Default when invoked without prior context |
Quick scan: identify relevant dimensions, 1-2 questions per dimension, answer with available context |
Risk Pulse: 2-3 bullets with dimension + signal |
| Standard (2) |
Medium risk signals (multiple modules, external deps, persistent data). After contextação reveals complexity. When orchestrator requests |
Each relevant dimension is actively investigated: search codebase, read docs, verify consumers, check breaking changes. Produces evidence |
Risk Radar: table of dimension × level × evidence × action |
| Deep (3) |
High risk signals (public API, production data, auth, irreversible). After validator discovers higher-than-expected risk. When orchestrator explicitly requests |
All dimensions evaluated. Online research (CVEs, migration guides, changelogs). Cross-reference between dimensions. Complete evidence |
Full Risk Radar + Mitigations + Accepted Risks |
Weight can escalate mid-analysis. If a Light scan reveals signals that warrant Standard, say so and escalate. The user or orchestrator can also request a specific weight directly.
Risk Dimensions — Catalogue (Not a Fixed Checklist)
The agent does NOT evaluate all dimensions every time. At Light weight, evaluate 0-2 most relevant. At Deep, evaluate all that apply. The request context determines which.
Core Dimensions
| Dimension |
Key Question |
Signals of Relevance |
| Security |
Does this change expose data, create injection vectors, or break auth? |
Touches auth, input parsing, user data, external APIs, crypto |
| Data Integrity |
Can data be corrupted, lost, or become inconsistent? |
Touches DB schemas, migrations, cache, file I/O, transactions |
| Backward Compatibility |
Will existing consumers break? |
Alters exported APIs, public schemas, contracts, response formats |
| Operational |
Can this cause downtime, resource exhaustion, or cascading failure? |
Alters queries, loops, connections, rate limits, retry logic |
| Reversibility (multiplier) |
If it goes wrong, can you undo it? What's the blast radius? |
DROP TABLE, secret push, production deploy, mutated data, public API consumed by third parties |
Reversibility as Multiplier
Reversibility is not a standalone risk — it's a multiplier on every other dimension. Low risk + irreversible = effectively HIGH risk.
The agent MUST challenge claims of reversibility. "Git revert solves it" is often false — production data was already mutated, consumers already consumed the broken API, secrets were already scraped. Ask: "After reverting the code, is the STATE also reverted?" If the answer is no, the risk is higher than it appears.
Contextual Dimensions
Beyond the 5 core dimensions, the agent can add domain-specific dimensions when context demands it:
- Cost — cloud resources, API call billing, storage growth
- Compliance — GDPR, HIPAA, PCI-DSS, data residency
- UX Impact — user-facing behavior changes, accessibility regressions
- Performance — latency, throughput, memory footprint
Add contextual dimensions only when the request context strongly signals them. Do not add them speculatively.
Procedure
Light (Weight 1)
- Read the request/context
- Identify which dimensions are relevant (0-2)
- For each: 1 quick question, check if you can answer with available context
- Produce Risk Pulse
Standard (Weight 2)
- Read request + output from prior skills (task-intent, contextação if available)
- Identify relevant dimensions (2-4)
- For each dimension: actively research — search codebase, read docs, verify consumers, check breaking changes
- Evaluate reversibility as multiplier
- Produce Risk Radar
Deep (Weight 3)
- Read all accumulated context
- Evaluate ALL 5 core dimensions + any relevant contextual dimensions
- For each: research online (changelogs, CVEs, migration guides, official docs). Search codebase exhaustively
- Cross-reference: does risk in one dimension affect another? (e.g., security fix that breaks backward compatibility)
- Evaluate reversibility of each risk individually
- Produce Full Risk Radar with Mitigations and Accepted Risks
Active Research Gate
For every risk identified, ask: "Can I verify this right now with available tools?"
- If yes → verify. Fetch the doc, search the repo, check the changelog, read the spec.
- If no → mark as
[unverified] with an explicit note on the impact of being wrong.
Prefer online and up-to-date sources. A risk you CAN verify but DON'T is an unforced error.
Output Formats
Risk Pulse (Light)
### Risk Pulse
- **[Dimension]**: [signal identified] — [action: proceed / investigate]
- **[Dimension]**: [signal identified] — [action: proceed / investigate]
Risk Radar (Standard / Deep)
### Risk Radar
| Dimension | Level | Evidence | Action |
|-----------|-------|----------|--------|
| Security | 🟢/🟡/🔴 | [what was verified] | proceed / investigate / block |
| Data Integrity | 🟢/🟡/🔴 | [what was verified] | ... |
| ... | ... | ... | ... |
| **Reversibility** | ↑ multiplier | [blast radius assessment] | ... |
### Mitigations (Standard/Deep only)
- [Risk]: [concrete mitigation — NOT generic]
### Accepted Risks (Deep only)
- [Accepted risk]: [explicit justification]
### Recommendation
[proceed / proceed with caution / stop and redesign]
Levels:
- 🟢 — No signal or verified safe
- 🟡 — Signal detected, manageable with mitigation
- 🔴 — Confirmed risk, requires action before proceeding
Companion Skills
- task-intent: Provides WHY/WHAT FOR context that informs which dimensions are relevant
- contextação: The Failure Modes axis from contextação can serve as initial seed. Safety-check expands with active dimensional research
- task-map: Accepted risks should be documented in the task-map for future reference
- error-learning: If an undetected risk causes a problem, register it as a lesson
Quality Checklist
1---2name: safety-check3description: **WORKFLOW SKILL** — Risk awareness before action. USE FOR: assessing risks (security, data integrity, compatibility, operational, reversibility) of any task at variable depth. Accepts weight: Light (quick scan), Standard (dimensional analysis), Deep (full Risk Radar with evidence). Can be invoked multiple times in the same flow with increasing weight. DO NOT USE FOR: penetration testing, compliance audits, static security analysis tools.4license: MIT5---67# Safety Check — Risk Awareness with Variable Weight89## The Problem You Solve1011Agents optimize for "it works" — but never ask "what breaks?". A rename is harmless. A schema change on a public API can break 50 consumers. A migration that drops a column destroys data that no `git revert` can recover. The agent doesn't distinguish between these scenarios because it has no risk radar.1213The typical mitigation is "be careful" — a vague instruction that produces vague caution. The agent either ignores it (nothing changes) or applies it uniformly (everything gets a warning, nothing gets real analysis). Both outcomes are useless.1415This skill replaces vague caution with **dimensional investigation at variable depth**. Instead of "be careful", the agent learns to ask "what can go wrong in THIS specific dimension, and can I verify it RIGHT NOW?". It transforms reflection into active research with evidence, scaling effort to match real risk — not perceived risk.1617## Weight Model1819The skill operates at three weights. The weight determines how deep the analysis goes. Start at Light by default; escalate when signals warrant it.2021| Weight | Trigger | What it does | Output |22|--------|---------|--------------|--------|23| **Light (1)** | Any non-trivial task. Default when invoked without prior context | Quick scan: identify relevant dimensions, 1-2 questions per dimension, answer with available context | Risk Pulse: 2-3 bullets with dimension + signal |24| **Standard (2)** | Medium risk signals (multiple modules, external deps, persistent data). After contextação reveals complexity. When orchestrator requests | Each relevant dimension is actively investigated: search codebase, read docs, verify consumers, check breaking changes. Produces evidence | Risk Radar: table of dimension × level × evidence × action |25| **Deep (3)** | High risk signals (public API, production data, auth, irreversible). After validator discovers higher-than-expected risk. When orchestrator explicitly requests | All dimensions evaluated. Online research (CVEs, migration guides, changelogs). Cross-reference between dimensions. Complete evidence | Full Risk Radar + Mitigations + Accepted Risks |2627Weight can escalate mid-analysis. If a Light scan reveals signals that warrant Standard, say so and escalate. The user or orchestrator can also request a specific weight directly.2829## Risk Dimensions — Catalogue (Not a Fixed Checklist)3031The agent does NOT evaluate all dimensions every time. At Light weight, evaluate 0-2 most relevant. At Deep, evaluate all that apply. The request context determines which.3233### Core Dimensions3435| Dimension | Key Question | Signals of Relevance |36|-----------|-------------|----------------------|37| **Security** | Does this change expose data, create injection vectors, or break auth? | Touches auth, input parsing, user data, external APIs, crypto |38| **Data Integrity** | Can data be corrupted, lost, or become inconsistent? | Touches DB schemas, migrations, cache, file I/O, transactions |39| **Backward Compatibility** | Will existing consumers break? | Alters exported APIs, public schemas, contracts, response formats |40| **Operational** | Can this cause downtime, resource exhaustion, or cascading failure? | Alters queries, loops, connections, rate limits, retry logic |41| **Reversibility** _(multiplier)_ | If it goes wrong, can you undo it? What's the blast radius? | `DROP TABLE`, secret push, production deploy, mutated data, public API consumed by third parties |4243### Reversibility as Multiplier4445Reversibility is not a standalone risk — it's a **multiplier** on every other dimension. Low risk + irreversible = effectively HIGH risk.4647The agent MUST challenge claims of reversibility. "Git revert solves it" is often false — production data was already mutated, consumers already consumed the broken API, secrets were already scraped. Ask: *"After reverting the code, is the STATE also reverted?"* If the answer is no, the risk is higher than it appears.4849### Contextual Dimensions5051Beyond the 5 core dimensions, the agent can add domain-specific dimensions when context demands it:5253- **Cost** — cloud resources, API call billing, storage growth54- **Compliance** — GDPR, HIPAA, PCI-DSS, data residency55- **UX Impact** — user-facing behavior changes, accessibility regressions56- **Performance** — latency, throughput, memory footprint5758Add contextual dimensions only when the request context strongly signals them. Do not add them speculatively.5960## Procedure6162### Light (Weight 1)63641. Read the request/context652. Identify which dimensions are relevant (0-2)663. For each: 1 quick question, check if you can answer with available context674. Produce **Risk Pulse**6869### Standard (Weight 2)70711. Read request + output from prior skills (task-intent, contextação if available)722. Identify relevant dimensions (2-4)733. For each dimension: **actively research** — search codebase, read docs, verify consumers, check breaking changes744. Evaluate reversibility as multiplier755. Produce **Risk Radar**7677### Deep (Weight 3)78791. Read all accumulated context802. Evaluate ALL 5 core dimensions + any relevant contextual dimensions813. For each: **research online** (changelogs, CVEs, migration guides, official docs). Search codebase exhaustively824. **Cross-reference**: does risk in one dimension affect another? (e.g., security fix that breaks backward compatibility)835. Evaluate reversibility of each risk individually846. Produce **Full Risk Radar** with Mitigations and Accepted Risks8586## Active Research Gate8788For every risk identified, ask: *"Can I verify this right now with available tools?"*8990- If **yes** → verify. Fetch the doc, search the repo, check the changelog, read the spec.91- If **no** → mark as `[unverified]` with an explicit note on the impact of being wrong.9293Prefer online and up-to-date sources. A risk you CAN verify but DON'T is an unforced error.9495## Output Formats9697### Risk Pulse (Light)9899```100### Risk Pulse101- **[Dimension]**: [signal identified] — [action: proceed / investigate]102- **[Dimension]**: [signal identified] — [action: proceed / investigate]103```104105### Risk Radar (Standard / Deep)106107```108### Risk Radar109110| Dimension | Level | Evidence | Action |111|-----------|-------|----------|--------|112| Security | 🟢/🟡/🔴 | [what was verified] | proceed / investigate / block |113| Data Integrity | 🟢/🟡/🔴 | [what was verified] | ... |114| ... | ... | ... | ... |115| **Reversibility** | ↑ multiplier | [blast radius assessment] | ... |116117### Mitigations (Standard/Deep only)118- [Risk]: [concrete mitigation — NOT generic]119120### Accepted Risks (Deep only)121- [Accepted risk]: [explicit justification]122123### Recommendation124[proceed / proceed with caution / stop and redesign]125```126127Levels:128- 🟢 — No signal or verified safe129- 🟡 — Signal detected, manageable with mitigation130- 🔴 — Confirmed risk, requires action before proceeding131132## Companion Skills133134- **task-intent**: Provides WHY/WHAT FOR context that informs which dimensions are relevant135- **contextação**: The Failure Modes axis from contextação can serve as initial seed. Safety-check expands with active dimensional research136- **task-map**: Accepted risks should be documented in the task-map for future reference137- **error-learning**: If an undetected risk causes a problem, register it as a lesson138139## Quality Checklist140141- [ ] Weight (Light/Standard/Deep) explicitly declared?142- [ ] Dimensions evaluated are contextual to the request (not a fixed checklist)?143- [ ] For each risk: concrete evidence or marked as `[unverified]`?144- [ ] Reversibility evaluated as multiplier?145- [ ] Recommendation is one of the 3 options (proceed / proceed with caution / stop and redesign)?146- [ ] At Deep weight: online research performed for external risks?
aibot88